The present invention relates generally to an optical implementation of a neural network for optical processing data. More particularly, the present invention relates to an optoelectronic neural network device which is compact and monolithic and which can provide flexible processing of data in optical form.
It is often desirable to be able to readily recognize patterns. Pattern recognition is a function which is important in both military and commercial applications, such as aided target recognition, speech recognition, machine recognition of handwriting, robotic vision, communications systems, automated control, design and manufacturing, radar data analysis, and other areas. At the present time, the most outstanding system for performing pattern recognition is the human brain which can recognize a pattern, even with partial or incomplete information and even in relatively ill-defined situations. Digital computers, which are capable of very fast number manipulation and other serial operations, are very inefficient in performing pattern-recognition tasks. In order to use a digital computer for performing pattern-recognition tasks, an enormous memory would be required, as well as a very large knowledge base and a rule-based expert system. Even so configured, such an electronic computer would only be able to perform relatively simple pattern-recognition tasks.
Current investigation of brain functions has contributed to artificial neural networks technology which is based upon the knowledge of how a biological brain functions as a parallel processor. A biological brain uses a large number of relatively simple but slow (millisecond range) processing elements or neurons which work in concert with each other with a massive number of interconnections. Such neural networks can be used to perform processing at several levels of pattern-recognition problems, including input data preprocessing, such as data recovery, noise removal, signal-to-noise-ratio (SNR) enhancement, signal and/or image segmentation through final target identification. Both electronic and optical approaches to hardware implementations of neural networks have been proposed.
Most of the optical implementations of neural networks have been based upon the use of a vector-matrix multiplication technique to simulate neural functioning. Most of those implementations utilize a free-space architecture and require many optical components which cannot be monolithically integrated. The present invention, on the other hand, implements optical neural architecture in GaAs materials which is very compact and monolithic and is inspired by the natural fiber-like structures common in all biological neurons. Utilizing the technology of semiconductor quantum wells and superlattices, which may also be used for optical modulators and switches, the present invention emulates the basic neural decision-making process of the human brain. Multiple inputs are accepted, different adjustable weights are applied to those inputs, the weighted inputs are summed, and then an optoelectronic thresholding switch is used to make a binary decision.
Biological brain systems appear to be successful due to their complex and massively parallel architectures. For example, a 3 pound human brain consists of over 10 billion neuron cells communicating among themselves through networks of over 100 trillion synaptic interconnections. Each neuron has a relatively simple structure and functioning method. Typically, a neuron consists of a cell body, ranging from about 5 to 100 microns in diameter, from which one major fiber, the axon, and a number of fibrous branches, the dendrites, emanate. The axon carries the outgoing signal from the neuron, and, near its end, it usually branches out extensively.
The dendrites receive the input signals from other neurons, where they form a contact, called a synapse. A weight is applied to each signal, and the cell body sums those weighted signals and then performs a simple thresholding operation based on that sum to determine whether to send a signal on to its axon or not. Generally speaking, a single neuron receives input from thousands of other neurons through its dendrites and similarly feeds its output to thousands of other neurons through its branching axon. The time scale involved, however, is relatively slow, being in the range of milliseconds.
An artificial neuron network consists of a massively interconnected network of processing elements. For purposes of mathematical simplification, such a network is divided into a number of layers; each layer is made up of a number of neurons or nodes. Every neuron in each of those layers is connected to every neuron in the next layer for a network that is fully connected. Alternatively, the number of connections could be less, depending upon the application. Every neural network, however, has an input and an output neural layer. There can also be some additional layers in between those two, which are known as hidden layers.
The operating principle of a neural network can be mathematically represented by a nonlinear function f, as set forth by R. P. Lippmann in "An introduction to computing with neural nets", IEEE ASSP Magazine, vol. 4 (April 1987).
While both electronic and optical approaches to the hardware implementation of neural networks are being used, optical implementations appear to have a very promising future because of the inherent parallelism of light. Most of the architecture suggested in the art by, for example, N. H. Farhat, "Optoelectronic neural networks and learning machine", IEEE Circuits and Devices Magazine, vol. 32 (September 1989); D. Psaltis, D. Brady, X. Gu, and K. Hsu, "Optical implementation of neural computers", Optical Processing and Computing, H. A. Arsenault, editor, chapter 8 (1989); Y. Nitta, J. Ohita, M. Takahashi, S. Tai, and K. Kyuma, "Optical neurochip with learning capability", Photonics Tech. Lett., vol. 4, No. 3, page 247 (1992), contain at least three planes. The first plane, corresponding to the input neurons, contains an array of discrete sources of light (either one- or two-dimensional), for example, spatial light modulators (SLM's) or light-emitting diodes.
The second layer contains a two-dimensional array of interconnection elements representing the weight matrix, for example, an SLM (either fixed or programmable) or a hologram (two-dimensional or volume). The third layer corresponds to the output neurons and contains an array of discrete photodetectors for summing the weighted inputs with a built-in quadratic nonlinearity. Sometimes an additional thresholding is provided at this stage using electronics or some other scheme. Some approaches also include the use of hidden layers. While most of those implementations are of the tabletop-type size, this type of architecture has recently been implemented in GaAs by Y. Nitta et al.
The present invention, in contrast, is an optical implementation of a feed-forward neural network architecture which is implemented in a monolithic GaAs/AlGaAs waveguide structure, in which both the weighting and thresholding are provided by the room-temperature macroscopic nonlinear behavior of an embedded superlattice by the application of small electric voltages.